Discover Agentic AI in the AI Agent Development Frameworks category: Comprehensive directory of solutions verified by AI agency professionals.

Agentic AI in "AI Agent Development Frameworks" Category

Awesome MCP Servers is a GitHub-curated list compiling various MCP (Model Context Protocol) servers that allow large language models to interface with external tools, files, APIs, databases, and more. These servers empower AI agents to perform tasks beyond simple text generation by enabling them to access structured resources and execute contextual operations. The list features both production-ready and experimental implementations contributed by the community.

Awesome MCP Servers

awesome-mcp-servers

A curated list of Model Context Protocol (MCP) servers enhancing AI capabilities with external tools and data.

kagent is an open-source framework designed for DevOps and platform engineers to deploy and manage AI agents within Kubernetes environments. It provides predefined tools for AI agents to interact with cloud-native projects like Kubernetes, Prometheus, Istio, Argo, and Helm. These autonomous agents are capable of planning, executing tasks, analyzing results, and continuously improving outcomes, thereby automating complex operations and troubleshooting tasks in cloud-native operations.

kagent

An open-source framework enabling AI agents to automate Kubernetes operations.

Claude MCP Agents are AI-driven entities developed by Anthropic, leveraging the Model Context Protocol (MCP) to facilitate seamless integration with diverse data sources and applications. MCP serves as an open standard, enabling AI systems to connect efficiently with external APIs, tools, and live data, thereby enhancing their functionality and contextual awareness. By utilizing MCP, Claude agents can perform tasks such as retrieving information, executing workflows, and interacting with various platforms, all while maintaining a cohesive understanding of the context. This integration simplifies the development process, allowing for rapid deployment of AI capabilities across multiple domains.

Claude MCP Agents

claude-mcp-agents

AI agents utilizing Anthropic's Model Context Protocol to integrate seamlessly with various data sources and tools.

Giselle is an open-source platform that empowers teams to design and deploy advanced AI agents using an intuitive node-based interface. By integrating multiple large language models (LLMs) and data sources, Giselle facilitates the automation of complex tasks such as market research and code reviews. Its visual development environment simplifies the creation of sophisticated AI workflows, making AI accessible to all team members and enhancing productivity through seamless human-AI collaboration.

Giselle

giselle

An open-source platform enabling teams to create AI agents through a visual node-based interface for automating complex workflows.

Gemma 3 is Google's latest open-source AI model, designed to run efficiently on a single GPU or TPU. It supports multimodal inputs, including text, images, and short videos, and offers advanced reasoning capabilities. With support for over 140 languages and a 128k-token context window, Gemma 3 is versatile for various applications. Its quantized versions enhance performance and reduce computational requirements, making it accessible for devices ranging from smartphones to workstations.

Gemma 3

gemma-3

An open-source AI model optimized for single-GPU performance, supporting multimodal inputs and over 140 languages.

L2MAC (Large Language Model Automatic Computer) is an AI framework designed to generate extensive outputs, such as entire codebases or lengthy texts, from a single prompt. It employs a multi-agent system where each agent executes a portion of the task, effectively bypassing the fixed context window constraints of traditional LLMs. This approach allows L2MAC to produce coherent and large-scale outputs, significantly enhancing the capabilities of AI in software development and content creation.

L2MAC

l2mac

An AI framework enabling large-scale code generation by overcoming LLM context limitations.

DemoGPT is an open-source initiative that automates the creation of Large Language Model (LLM)-based applications. Leveraging GPT, it translates user instructions into LangChain code, which is then rendered as interactive Streamlit applications. The process involves planning, task creation, code snippet generation, and final code assembly, ensuring a seamless transition from concept to functional application. DemoGPT's flexibility allows it to utilize any LLM model meeting specific performance criteria, keeping it at the forefront of AI application development.

DemoGPT

demogpt

An open-source tool that transforms user instructions into interactive Streamlit applications using GPT.

BondAI is an open-source tool designed for developing sophisticated AI agent systems. It handles complex implementation aspects such as memory and context management, error handling, and semantic search capabilities. Supporting advanced AI architectures like ReAct Agents and Conversational Agents, BondAI enables both individual interactions and multi-agent collaborations. The framework integrates seamlessly with services like OpenAI models, Microsoft Azure, Google Search, and LangChain, providing a versatile environment for AI development.

BondAI

bondai

Open-source framework for developing AI agent systems with advanced memory management and integrations.

BabyCommandAGI is an open-source AI agent designed to explore the integration of Command-Line Interface (CLI) and Large Language Models (LLMs) for autonomous command execution. Built upon the BabyAGI framework, it leverages models like GPT-4o to interpret objectives, plan tasks, and execute commands within a CLI environment. This integration aims to automate complex workflows, including programming, environment setup, and data retrieval, by enabling dynamic interactions between LLMs and traditional CLI operations.

BabyCommandAGI

babycommandagi

An AI agent that integrates CLI and LLM to autonomously execute commands towards defined objectives.

BabyElfAGI is an advanced modification of the BabyAGI series, introducing a Skills class that allows for the creation of new skills, enhancing the agent's capabilities. It features a 'dynamic task list' example with vector search and includes a beta reflection agent, enabling the AI to read, write, and review its own code. With approximately 895 lines of code, BabyElfAGI represents a significant evolution in autonomous AI agent development.

BabyElfAGI

babyelfagi

An advanced AI agent framework introducing a Skills class for dynamic skill creation and task management.

BabyDeerAGI is a lightweight modification of BabyCatAGI, itself an evolution of BabyBeeAGI and the original BabyAGI framework. With approximately 350 lines of code, BabyDeerAGI introduces parallel task execution, enhancing the efficiency and speed of autonomous task management. It operates using OpenAI's GPT-3.5-turbo model, eliminating the need for GPT-4, and incorporates features such as user input tools, query rewriting in web search, and result saving functionalities. This streamlined approach facilitates faster and more efficient task automation.

BabyDeerAGI

babydeeragi

A streamlined AI agent framework enabling parallel task execution for enhanced efficiency.

BabyCatAGI is a lightweight modification of BabyBeeAGI, itself an evolution of the original BabyAGI framework. With approximately 300 lines of code, BabyCatAGI introduces a task creation agent that operates once at the beginning, streamlining task execution. It features an intelligent agent tool combining web search, scraping, chunking, and extraction to enhance information retrieval and processing, resulting in faster and more stable task management.

BabyCatAGI

babycatagi

A streamlined AI agent framework enhancing task automation and execution efficiency.

Browse Agentic AI Category "AI Agent Development Frameworks"